{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exponential Modeling of COVID-19 Confirmed Cases\n",
    "This notebook explores modeling the spread of COVID-19 confirmed cases as an exponential function. While this is not a good model for long or even medium-term predictions, it is able to fit initial outbreaks quite well. For a more sophisticated and accurate model, see the [logistic modeling](logistic_modeling.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "df = pd.read_csv('https://open-covid-19.github.io/data/data.csv')\n",
    "df = df.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining our parameters\n",
    "Here we are looking at the confirmed and fatal cases for Italy through March 17. To apply the model to other countries or dates, just change the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_code = 'IT'\n",
    "date_limit = '2020-03-17'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at the outbreak\n",
    "There are months of data, but we only care about when the number of cases started to grow. We define *outbreak* as whenever the number of cases exceeded certain threshold. In this case, we are using 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_outbreak_mask(data: pd.DataFrame, threshold: int = 10):\n",
    "    ''' Returns a mask for > N confirmed cases '''\n",
    "    return data['Confirmed'] > threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['CountryCode', 'CountryName', 'Confirmed', 'Deaths']\n",
    "# Get data only for the selected country\n",
    "country = df[df['CountryCode'] == country_code][cols]\n",
    "# Get data only for the selected dates\n",
    "country = country[country.index <= date_limit]\n",
    "# Get data only after the outbreak begun\n",
    "country = country[get_outbreak_mask(country)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the data\n",
    "Let's take a first look at the data. A visual inspection will typically give us a lot of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "country.plot(kind='bar', figsize=(16, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling the data\n",
    "The data appears to follow an exponential curve, it looks straight out of a middle school math textbook cover. Let's see if we can model it using some parameter fitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Estimated function: 643.059 * (1.173 ^ X) + -968.496\n"
    }
   ],
   "source": [
    "from scipy import optimize\n",
    "\n",
    "def exponential_function(x: float, a: float, b: float, c: float):\n",
    "    ''' a * (b ^ x) + c '''\n",
    "    return a * (b ** x) + c\n",
    "\n",
    "X, y = list(range(len(country))), country['Confirmed'].tolist()\n",
    "params, _ = optimize.curve_fit(exponential_function, X, y)\n",
    "\n",
    "print('Estimated function: {0:.3f} * ({1:.3f} ^ X) + {2:.3f}'.format(*params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "ax = country[['Confirmed']].plot(kind='bar', figsize=(16, 8))\n",
    "estimate = [exponential_function(x, *params) for x in X]\n",
    "ax.plot(country.index, estimate, color='red', label='Estimate')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validating the model\n",
    "That curve looks like a very good fit! Even though proper epidemiology models are fundamentally different (because diseases can't grow exponentially indefinitely), the exponential model should be good for short term predictions.\n",
    "\n",
    "To validate our model, let's try to fit it again without looking at the last 3 days of data. Then, we can estimate the missing days using our model, and verify if the results still hold by comparing what the model thought was going to happen with the actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "ESTIMATE_DAYS = 3\n",
    "params_validate, _ = optimize.curve_fit(exponential_function, X[:-ESTIMATE_DAYS], y[:-ESTIMATE_DAYS])\n",
    "\n",
    "# Project zero for all values except for the last ESTIMATE_DAYS\n",
    "projected = [0] * len(X[:-ESTIMATE_DAYS]) + [exponential_function(x, *params_validate) for x in X[-ESTIMATE_DAYS:]]\n",
    "projected = pd.Series(projected, index=country.index, name='Projected')\n",
    "df_ = pd.DataFrame({'Confirmed': country['Confirmed'], 'Projected': projected})\n",
    "ax = df_.plot(kind='bar', figsize=(16, 8))\n",
    "estimate = [exponential_function(x, *params_validate) for x in X]\n",
    "ax.plot(country.index, estimate, color='red', label='Estimate')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projecting future data\n",
    "It looks like our exponential model slightly overestimates the confirmed cases. That's a good sign! It means that the disease is slowing down a bit. The numbers are close enough that a 3-day projection is probably an accurate enough estimate.\n",
    "\n",
    "Now, let's use the model we fitted earlier which used all the data, and try to predict what the next 3 days will look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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WdGK/P//8Q5dcEn/ScV6FhtpOyXDsWLqio2MkmU7MNplMMpstF3OqAAAAAMoI86GDipg1Xc6Uh+W+qbbRcU7BZcZ+pE6dW7Rly2Y5HJmSpDVrVqlOnYRT9qlb91atWrVcknTkyBE9+eTjOnTooGrXrqP3339HPp9Px48f07PPtpPLVSCLxSqPxyO73a7LLrtcmzatlyRt2/aZOnR4RpKUkHDbie0fffS+CgryS+uUAQAAAPixiPFjJLdbOf0GGx3lNJRZP3LVVVerZcun9Oyz7dSixUPKzs5Su3YdT9mnTZu2ys/PV8uWj6hbtw7q1KmrqlS5TA8+2FxhYWFq3fpxPfdcJ3Xv3lsREZH6xz9uV1raaH377X80dOhIrV27Sk8++ZhmzZqukSPHyGQyqUePPvroo/f15JOP69NPP1FERKRBrwAAAAAAf2H59ReFLX5VeU+2kbfalUbHOU1QXGbsLsj/79foFP/zFkblypdq+fK1Z3zs79uTkx9QcvIDp2zLzs5WeHi4JCky0q4hQ07/TGtoaKj69Bl42vYWLVqqRYuWJ/48bdrsE7+3Ws1yu72Ki6ukF1+cdWJ7//5DCnFWAAAAAMqyyBeely8sXLnd+xgd5YyCoswezyzQmb4P9n9lzp8dPHhA7du3VlJSM6OjAAAAAAgS1u1fyLZutXL6DJAvLs7oOGcUFGU2kMXHV9bq1ZuMjgEAAAAgWPh8ihwxVN6Kccrt8KzRac6Kz8wCAAAAAE4Ife9thX76iXJ69pXsdqPjnFWZLbM+39+/5gbFzefz6uSvAwIAAAAQ4DweRY4YJk+1K+Vs2droNOdUJi8ztlpDlZPjUGRktEwmylZx8/l88njcyso6rtDQMKPjAAAAACgmtuVLZd35vRyz50mhoUbHOacyWWbLlYvT8ePpys7OOOd+ZrNZXq9xN4AK5Plms0Xh4XbZ7THFnAoAAACAIZxORY4dJdfNdZTf9EGj05xXmSyzFotVFStWPu9+cXFRSk/PKoVEzAcAAADg38Lnz5Hlzz+UNXm6ZPb/T6T6f0IAAAAAQIkyOTIVMWm8Cv75L7nq/9PoOIVCmQUAAACAIBc+bYrMx48rZ/Bwo6MUGmUWAAAAAIKY+eABRbw0Xc6U5nLfeLPRcQqNMgsAAAAAQSxi/BjJ7VZOv0FGRykSyiwAAAAABCnLLz8rbMkC5T3ZRt5qVxodp0goswAAAAAQpCJfeF6+sHDldu9jdJQio8wCAAAAQBCybv9CtrfWKK9zV/ni4oyOU2SUWQAAAAAINj6fIp8fIm/FOOV2eNboNBeEMgsAAAAAQSb03U0K/Wyrcnr1k+x2o+NcEMosAAAAAAQTj0eRI4fLfWV1OVu2NjrNBbMaHQAAAAAAUHpsy5fKuvN7OV6eL4WEGB3ngrEyCwAAAADBwulU5NhRctWuo/zkB4xOc1FYmQUAAACAIBE+b44sf/6hrCkzJHNgr20GdnoAAAAAQKGYMjMUMXm8Cv75L7nuTjQ6zkWjzAIAAABAEIiYNkXm48eVM3i40VGKBWUWAAAAAMo484H9Cp89Q86U5nLfeLPRcYoFZRYAAAAAyriItDGS262cfoOMjlJsKLMAAAAAUIZZfvlZYYsXKK/10/JWu9LoOMWGMgsAAAAAZVjkqOHyRUQqt3sfo6MUq0KX2bFjx6pfv36SpJ07dyolJUUNGzbUwIED5Xa7JUn79+9XamqqGjVqpI4dOyonJ0eS5HA41K5dOzVu3FipqalKT0+XJBUUFKh3795q3LixHnzwQf3222/FfX4AAAAAELw++0y29WuV17mrfBUrGp2mWBWqzH766adauXLliT/37t1bQ4YM0aZNm+Tz+bRs2TJJ0vDhw9WiRQtt3LhRtWrV0owZMyRJkydPVkJCgjZs2KDmzZtr1KhRkqSFCxcqPDxcGzZs0IABA9S/f//iPj8AAAAACE4+n9Snj7xxlZTbvrPRaYrdectsRkaGJk2apA4dOkiS9u3bJ6fTqdq1a0uSUlJStHHjRrlcLm3btk0NGzY8Zbskffjhh0pOTpYkNWnSRJs3b5bL5dKHH36opk2bSpJuvfVWHTt2TPv37y/+swQAAACAIBO64S3p44+V06ufZLcbHafYWc+3w5AhQ9S9e3cdOHBAknT48GHFxcWdeDwuLk6HDh3S8ePHZbfbZbVaT9n+92OsVqvsdruOHTt2xuc6ePCgLr300kKfQIUKF/cfJS4u6qKOv1jMZz7zg28285nP/OCdH8znznzmM7+U5zsc0sDe8tWsqajuzyoqJOSMu3ndBTJbQ0s3299c6GtzzjL7xhtvqHLlyrr99tv15ptvSpK8Xq9MJtOJfXw+n0wm04lfT/b3P598jNlsPu2Y/20viqNHs+X1+op0zP/ExUUpPT3rgo4tDsxnPvONmR/M58585jOf9x7mM5/5wTE/ckAfhe/fL9Py5do17rGz7ld94IoSzVaYonq2+Waz6ZyLl+css+vXr1d6erqaNWumzMxM5ebmymQynbiBkyQdOXJElSpVUvny5ZWVlSWPxyOLxaL09HRVqlRJklSpUiUdOXJE8fHxcrvdysnJUWxsrC655BIdPnxYV1xxxSnPBQAAAAC4MNYd2xT+ymw527RVeL160ntGJyoZ51wGnTdvntatW6fVq1era9eu+te//qXRo0fLZrNpx44dkqTVq1erfv36CgkJUUJCgtavXy9JWrVqlerXry9JSkxM1KpVqyT9VZATEhIUEhKixMRErV69WpK0fft22Wy2Il1iDAAAAAA4iculqJ7d5I2vrJwBQ4xOU6LO+5nZM0lLS9OgQYOUnZ2tmjVrqlWrVpKkoUOHql+/fpo5c6YqV66siRMnSpK6deumfv36KSkpSVFRUUpLS5MktWzZUkOGDFFSUpJCQ0M1bty4YjotAAAAAAg+4TOnyfrDd8qcv0S+qGij45SoQpfZlJQUpaSkSJKuu+46LV++/LR9qlSpooULF562PTY2VrNmzTptu81m09ixY4uSFwAAAABwBubduxSZNlr59yer4P4mRscpcUW72xIAAAAAwP/4fIrq3V0+a4iyR483Ok2puKDLjAEAAAAA/sO2fKlCN3+grDET5K0cHPchYmUWAAAAAAKY6ehR2Yf0lyvhNjlbP210nFJDmQUAAACAAGYfNlCmzExlTXhRMgdPxQueMwUAAACAMiZk84cKW7pEuc8+J8/1Nxgdp1RRZgEAAAAgEOXlKapXN7mvrK7c7r2NTlPquAEUAAAAAASgiEnjZdmzWxkr1krh4UbHKXWszAIAAABAgLH88L0ipk2W87FUue5ONDqOISizAAAAABBIPB5F9ewiX0yMsoeNNDqNYbjMGAAAAAACSNj8VxSyY7sc02fLV76C0XEMw8osAAAAAAQI84H9ihw1XAWJ9yj/4UeNjmMoyiwAAAAABAh7/94yedzKGj9ZMpmMjmMoyiwAAAAABIDQt9bKtn6tcnr2k7falUbHMRxlFgAAAAD8nCnLIXv/XnLfUEt5HZ81Oo5f4AZQAAAAAODnIkcNl/nQQWXMXyyFhBgdxy+wMgsAAAAAfsy6/QuFzZujvGfay103weg4foMyCwAAAAD+yuVSVM+u8la+VLn9Bxudxq9wmTEAAAAA+KnwGS/KuvMHZS54XT57lNFx/AorswAAAADghyy7flVk2hjlN2mmgkb3Gx3H71BmAQAAAMDf+Hyy9+4uX6hN2S+MMzqNX+IyYwAAAADwM7Zlryn044+UNW6SvPGVjY7jl1iZBQAAAAA/YjpyRPahA+S69R9ytnrK6Dh+izILAAAAAH7EPnSATFlZyprwomSmsp0NrwwAAAAA+ImQD99X2BuvK7fLc/Jcd73RcfwaZRYAAAAA/EFurqJ6Pyd39RrKfa630Wn8HjeAAgAAAAA/EDlxnCx79yhj5VtSWJjRcfweK7MAAAAAYDDL998pfPoU5bVoKdeddxsdJyBQZgEAAADASB6Ponp2ka9cOeUMHWF0moDBZcYAAAAAYKCweS8r5MsdcsycI1+58kbHCRiszAIAAACAQcz7/lTkqOdVcM+9yk9pbnScgEKZBQAAAAAj+Hyy9+8lk9ejrHGTJJPJ6EQBhTILAAAAAAYIfWutbBvXK6fPQHmrVjM6TsChzAIAAABAKTM5MmXv30uuWjcpr30no+MEJG4ABQAAAAClLHLkMJnTD8ux4DXJSi27EKzMAgAAAEApsn7xucJenau8th3krnOL0XECFmUWAAAAAEpLQYGienWVt8plyuk7yOg0AY31bAAAAAAoJRHTp8j6405lLl4m2e1GxwlorMwCAAAAQCmw/PaLIiaOk7Ppgyq4r5HRcQIeZRYAAAAASprPJ3vv7vLZwpQzaqzRacoELjMGAAAAgBJmW7pEoVs2KyttiryXxBsdp0xgZRYAAAAAStLhw7IPHSDXP26X84knjU5TZlBmAQAAAKAk9eghU3a2sia8KJmpYMWFVxIAAAAASkjI++9Kixcrt2sPea651ug4ZQplFgAAAABKQk6Oovr0kK69VrndehqdpszhBlAAAAAAUALsA/vI/Mdeed97R3GXx511P3dBvo5nFpRisrKBMgsAAAAAxcz2xusKX7JQOT16K/Kee7Vr1ENn3bf6wBWSKLNFxWXGAAAAAFCMLL/9oqje3VVQ7w7l9upvdJwyizILAAAAAMXF6VT0M63lC7Mpa9YrkpWLYUsKrywAAAAAFBP70AGyfv+tMhcvk/fSKkbHKdNYmQUAAACAYhC6drXC581RbscuKrivkdFxyjzKLAAAAABcJPPePYrq/qxcdW9RzsChRscJCpRZAAAAALgYBQWKbv+UJMnx0jwpNNTgQMGBz8wCAAAAwEWIHDVcIV/uUOYrC+WtWs3oOEGDlVkAAAAAuEChb29QxMypymvTVgXJzYyOE1QoswAAAABwAcz79ymqSwe5at2k7GGjjI4TdCizAAAAAFBUbrei27eRClzKenmeFBZmdKKgw2dmAQAAAKCIIsa/oJDPP5Vjxsvy1Lja6DhBiZVZAAAAACiCkI8+UMTkCcpr0VL5Dz9qdJygRZkFAAAAgEIyHTqk6E5t5bnmWmWPGmd0nKDGZcYAAAAAUBgej6I7tZUpO0uOFWulyEijEwU1yiwAAAAAFELEixMV+vGHypo0TZ7rrjc6TtDjMmMAAAAAOI+Qz7YqYuwoOVOay9mipdFxIMosAAAAAJyT6ehRRbVvI0/VaspOmyyZTEZHgrjMGAAAAADOzudTVNcOMh89oowN78lnjzI6Ef6LMgsAAAAAZxE+a7ps72xS1ujxct94s9FxcBIuMwYAAACAM7B+uV2RI4Yo//5kOdu0MzoO/oYyCwAAAAB/Y8rMUHS7p+StfKmyJk/jc7J+iMuMAQAAAOBkPp+iuneRef8+ZazZKF9sOaMT4QxYmQUAAACAk4TNmyPbutXKGThM7oTbjI6Ds6DMAgAAAMB/Wb79RvYh/ZX/7wbK6/is0XFwDpRZAAAAAJBkys5SdLvW8pavoKypL0lm6pI/4zOzAAAAAODzyd67uyy7dynzzXXyVahgdCKcB/+rAQAAAEDQs72+WGErlim3d3+57rjL6DgoBMosAAAAgKBm+elHRfXrqYK7E5X7XC+j46CQClVmp0yZovvvv19JSUmaN2+eJGnr1q1KTk5WgwYNNGnSpBP77ty5UykpKWrYsKEGDhwot9stSdq/f79SU1PVqFEjdezYUTk5OZIkh8Ohdu3aqXHjxkpNTVV6enpxnyMAAAAAnFlurqLbPilfpF1ZM16WLBajE6GQzltmv/jiC3322Wdas2aNVqxYoYULF+rHH3/UgAEDNGPGDK1fv17fffedPvroI0lS7969NWTIEG3atEk+n0/Lli2TJA0fPlwtWrTQxo0bVatWLc2YMUOSNHnyZCUkJGjDhg1q3ry5Ro0aVYKnCwAAAAD/zz6oryw//SjHjJflvSTe6DgogvOW2dtuu00LFiyQ1WrV0aNH5fF45HA4VLVqVV1++eWyWq1KTk7Wxo0btW/fPjmdTtWuXVuSlJKSoo0bN8rlcmnbtm1q2LDhKdsl6cMPP1RycrIkqUmTJtq8ebNcLldJnS8AAAAASJJsb4jitDwAACAASURBVL6h8EWvKrdbT7n++S+j46CICnWZcUhIiF588UUlJSXp9ttv1+HDhxUXF3fi8UqVKunQoUOnbY+Li9OhQ4d0/Phx2e12Wa3WU7ZLOuUYq9Uqu92uY8eOFdsJAgAAAMDfWXb9KnvPbnLdVk+5fQYYHQcXoNBfzdO1a1e1bdtWHTp00J49e2QymU485vP5ZDKZ5PV6z7j9f7+e7O9/PvkYcxG+z6lCBXuh9z2TuLioizr+YjGf+cwPvtnMZz7zg3d+MJ8785nvV/Pz86WOT0u2UJmXL1Nc5XLGBfsv3puK7rxl9rffflNBQYGuv/56hYeHq0GDBtq4caMsJ30wOj09XZUqVVJ8fPwpN3A6cuSIKlWqpPLlyysrK0sej0cWi+XE/tJfq7pHjhxRfHy83G63cnJyFBsbW+gTOHo0W16vryjnfEJcXJTS07Mu6NjiwHzmM9+Y+cF87sxnPvN572E+85kvRQ7orYivvlLmwqUqCIuVSjhbYcpaSb0+Rs6+2Plms+mci5fnXQL9888/NWjQIBUUFKigoEDvvfeeHnvsMe3evVt79+6Vx+PRunXrVL9+fVWpUkU2m007duyQJK1evVr169dXSEiIEhIStH79eknSqlWrVL9+fUlSYmKiVq1aJUlav369EhISFBISct4TBgAAAICiCn1rrSLmvKTc9p1V0LCx0XFwEc67MpuYmKhvvvlGDzzwgCwWixo0aKCkpCSVL19eXbp0UX5+vhITE9WoUSNJUlpamgYNGqTs7GzVrFlTrVq1kiQNHTpU/fr108yZM1W5cmVNnDhRktStWzf169dPSUlJioqKUlpaWgmeLgAAAIBgZf59r6Ke6yxX7TrKGTzc6Di4SIX6zGyXLl3UpUuXU7bdfvvtWrNmzWn7XnfddVq+fPlp26tUqaKFCxeetj02NlazZs0qbF4AAAAAKDqXS9Htn5K8Xjlmz5dCQ41OhItU6BtAAQAAAECginzheYXs2K7MOa/KW+1Ko+OgGBT+tsEAAAAAEIg2bFDE9CnKe/JpFTR90Og0KCaUWQAAAABllvnAfqlVK7lvqKXs518wOg6KEWUWAAAAQNnkcimq4zNSXp4cL8+XwsONToRixGdmAQAAAJQ9Pp/sfXsodOsWaeFCea6+xuhEKGaszAIAAAAoc8JfnKjwRa8qp3sv6YknjI6DEkCZBQAAAFCm2FYsk33UcDlTmiu332Cj46CEUGYBAAAAlBkhn36iqG6dVHDHXcqaMkMymYyOhBJCmQUAAABQJlh++VnRTz4uT9VqcsxfLNlsRkdCCaLMAgAAAAh4psOHFfP4w5I1RJlLlssXW87oSChh3M0YAAAAQGDLzVVMq0dlTj+kjFXr5a1azehEKAWUWQAAAACBy+NR+S7tZPnqS2nlSpVr8M/TdilweUo/F0ocZRYAAABAwIocOkCWtWv00j3PaN1mSZtXn7bP2gnNSj8YShxlFgAAAEBACp89QxGzZ0rdu2udKdHoOChl3AAKAAAAQMAJfWutIgf3V/79ydL48UbHgQEoswAAAAACinXHNkV3fFruurfIMeNlyWIxOhIMQJkFAAAAEDDMe3YrpuWj8l4Sr8wFS6WICKMjwSCUWQAAAAABwXT8mGJaPCx5PMp8fYV8cXFGR4KBuAEUAAAAAP/ndCr6yRay/L5XmcvXyFPjaqMTwWCUWQAAAAD+zetVVLeOCv1sqxyz58lV7w6jE8EPcJkxAAAAAL8WOXqEwlauUPag4cp/4CGj48BPUGYBAAAA+K2wBfMUMWWC8lq1UV6X54yOAz9CmQUAAADgl0Lfe1v2vj2Uf+99yh6TJplMRkeCH6HMAgAAAPA7lm+/UdQzreW+oZayXp4vWbndD05FmQUAAADgV8z7/lRManP5YmPlWLxMPnuU0ZHgh/jfGwAAAAD8hsmRqZgWzWXKyVHG2k3yxlc2OhL8FGUWAAAAgH9wuRT9dCtZfvlJma+tkOeGmkYngh+jzAIAAAAwns8ne69uCv3oAzlenClX4j1GJ4Kf4zOzAAAAAAwXMWm8wl9bpJyefZX/WKrRcRAAKLMAAAAADGV743VFjhkpZ/PHlNtngNFxECAoswAAAAAME7Jls6Ke66yCu+ora9I0vksWhUaZBQAAAGAIy08/Krp1qjzVa8gxb5EUGmp0JAQQyiwAAACAUmc6dEgxLR6WLyxMmUuWyxcTa3QkBBjuZgwAAACgdOXkKOaJR2Q+ekQZqzfIe/kVRidCAKLMAgAAACg9Ho+iO7SR9dv/yLHgNblvrmN0IgQoyiwAAACA0uHzyT6wj2ybNihrzAQVNGhsdCIEMD4zCwAAAKBUhM+arvC5Lyu3U1c527Q1Og4CHCuzAAAAAC5KVHS4wmxnrxYFLo9C165W5LCByk9+QDlDni/FdCirKLMAAAAALkqYzarknqvP+vjahysppHNbuW+5VY5pL0lmLhDFxeNvEQAAAIASU/n4AalpU3kqX6rMhUul8HCjI6GMoMwCAAAAKBHls45q2MrnJZ9Pma+tkK9CBaMjoQzhMmMAAAAAxS7Oka6RbwxWbF6m9NH78lavYXQklDGszAIAAAAoVpUyD2n0soGKdjo0+KHhUr16RkdCGcTKLAAAAIBiE59xQC+8MVhhBU4Nfuh5/Rp/ldGRUEZRZgEAAAAUi0uP79OoN4Yo1F2ggc2f1+5K1Y2OhDKMMgsAAADgol129A+NXD5EFq9XA5qP0N64akZHQhlHmQUAAABwcb77Ti+8MUiSSQMeGak/KlxudCIEAW4ABQAAAOCCWb77VrrnHnlNFvV/ZBRFFqWGlVkAAAAAF8T6zdeKad5MioxU/+SBOlCustGREERYmQUAAABQZNavdijmoaby2aOkzZspsih1lFkAAAAARWLd9rliHm4mX2ysMlatl6680uhICEKUWQAAAACFFvLZVsU88qC8FSsqY/UGeS+/wuhICFKUWQAAAACFEvLJx4p5LEXeypWVuXqDvJdWMToSghhlFgAAAMB5hXz0gWJaPCzP5VcoY9UGeeP5jCyMRZkFAAAAcE4h77+jmCcekadadWWsXC9fpUpGRwIoswAAAADOLvTtDYpp9bjc11ynjJXr5KtY0ehIgCTKLAAAAICzCF2/TtFPPSH3DTWVuWKNfOUrGB0JOIEyCwAAAOA0oWtXKfqZVnLfdLMy31gtX2w5oyMBp6DMAgAAADiFbeVyRbd7Su66Ccpctkq+mFijIwGnocwCAAAAOMG27DVFdXxGrn/crozX35QvKtroSMAZUWYBAAAASJLClixUVJcOct15tzIXvyHZ7UZHAs6KMgsAAABAYQvmKeq5znIl3qPMRcukyEijIwHnRJkFAAAAglzYK7MV1aub8u9rqMwFr0vh4UZHAs6LMgsAAAAEsfCXpiuqfy/lN0qSY+4iKSzM6EhAoVBmAQAAgCAVPm2K7IP7K79JMznmvCrZbEZHAgrNanQAAAAAAKUvYtJ4RY4eIecDKcqa/rIUEmJ0JKBIKLMAAABAMPH5FJE2RpHjR8v50CPKmjpLslILEHj4WwsAAAAEC59PEWNGKHJSmpyPpSpr0jTJYjE6FXBBKLMAAABAgIuKDleY7ew/2he4PJLPp8jnhyhi+hTltWyt7PGTJTO30EHgoswCAAAAAS7MZlVyz9VnfXxtWlNFDumviJdmKK/108oeM4Eii4DH32AAAACgDDP5vFKXLop4aYZy23VU9tiJFFmUCazMAgAAAGWU2etRp3dnSd+9o9xOXZUzdIRkMhkdCygWlFkAAACgDIp0ZqvvuvGq8/t/pIEDldO1D0UWZQrXFwAAAABlzKXH9ynttT6q9ef3mtygizRyJEUWZU6hyuy0adOUlJSkpKQkjRs3TpK0detWJScnq0GDBpo0adKJfXfu3KmUlBQ1bNhQAwcOlNvtliTt379fqampatSokTp27KicnBxJksPhULt27dS4cWOlpqYqPT29uM8RAAAACBo3/f4fTVjSR1HObA16eLjeq3Wv0ZGAEnHeMrt161Zt2bJFK1eu1KpVq/T9999r3bp1GjBggGbMmKH169fru+++00cffSRJ6t27t4YMGaJNmzbJ5/Np2bJlkqThw4erRYsW2rhxo2rVqqUZM2ZIkiZPnqyEhARt2LBBzZs316hRo0rwdAEAAICyq9F/Nur5FcN11F5BPVuM1w+X1TQ6ElBizltm4+Li1K9fP4WGhiokJEQ1atTQnj17VLVqVV1++eWyWq1KTk7Wxo0btW/fPjmdTtWuXVuSlJKSoo0bN8rlcmnbtm1q2LDhKdsl6cMPP1RycrIkqUmTJtq8ebNcLldJnS8AAABQ9rjdavf+bHV+b5a+rFZHvR8bo0MxlxidCihR5y2zV1999YlyumfPHm3YsEEmk0lxcXEn9qlUqZIOHTqkw4cPn7I9Li5Ohw4d0vHjx2W322W1Wk/ZLumUY6xWq+x2u44dO1Z8ZwgAAACUYabMDCkpSclfr9fKW5pqZLMByrNFGB0LKHGFvpvxL7/8ovbt26tPnz6yWCzas2fPicd8Pp9MJpO8Xq9MJ32w/H/b//fryf7+55OPMRfhe68qVLAXet8ziYuLuqjjLxbzmc/84JvNfOYzP3jnB/O5M7+E5v/6q9SkibRrl168r7PeufG+0p1fBME+/3x4byq6QpXZHTt2qGvXrhowYICSkpL0xRdfnHKjpvT0dFWqVEnx8fGnbD9y5IgqVaqk8uXLKysrSx6PRxaL5cT+0l+rukeOHFF8fLzcbrdycnIUGxtb6BM4ejRbXq+v0PufLC4uSunpWRd0bHFgPvOZb8z8YD535jOf+bz3ML/szA/ZslnRbZ6QzGaZ33lH76zJOOf+JXn+hSkjZXn++RiZz+jX5mLmm82mcy5enncJ9MCBA+rcubPS0tKUlJQkSbr55pu1e/du7d27Vx6PR+vWrVP9+vVVpUoV2Ww27dixQ5K0evVq1a9fXyEhIUpISND69eslSatWrVL9+vUlSYmJiVq1apUkaf369UpISFBISMh5TxgAAAAIVmGvzlXMIw/Ie0m8jm/8QEpMNDoSUOrOuzL7yiuvKD8/X2PGjDmx7bHHHtOYMWPUpUsX5efnKzExUY0aNZIkpaWladCgQcrOzlbNmjXVqlUrSdLQoUPVr18/zZw5U5UrV9bEiRMlSd26dVO/fv2UlJSkqKgopaWllcR5AgAAAIHP7VbksIGKmD1T+ffep6yX5soXHWN0KsAQ5y2zgwYN0qBBg8742Jo1a07bdt1112n58uWnba9SpYoWLlx42vbY2FjNmjWrMFkBAACAoGVyZCq6bWuFfvCectt3Us6wUZLFYnQswDCFvgEUAAAAAGOYd+9STMtHZdn1m7ImvChny9ZGRwIMR5kFAAAA/FjI1i2KfipVkpT5xmq57rzb4ESAfyj8d+AAAAAAKFVhi15VzMNN5a0Yp+Mb3qfIAiehzAIAAAD+xuNR5OB+iurRRa67E5Wx/l15q9cwOhXgV7jMGAAAAPAjpiyHoto9Jdt77yi3bQflDH9BsvJjO/B3/KsAAAAA/IR5z+6/bvT06y/KGjdJztZPGx0J8FuUWQAAAMAPhHz6yV83evJ6lblslVx3JxodCfBrfGYWAAAAMFjYkoV/3eipXHllbHyfIgsUAmUWAAAAMIrHo8ihAxX1XGe5br9LGRvek6f6VUanAgIClxkDAAAABjBlORTV4WnZ3tmkvKfbKXvEGG70BBQB/1oAAACAUmbeu+evGz398rOyxkyQs01boyMBAYcyCwAAAJQi62efKuapFpLbo8zX35Qr8R6jIwEBic/MAgAAAKXE9vpixT7URN6YWGVsfI8iC1wEyiwAAABQ0jweRQ4frOiuHeWqd6cyNr4vT42rjU4FBDQuMwYAAAAuUlR0uMJsZ/nROitL3mYPKOKtdcpr/bSyR42TQkJKNyBQBlFmAQAAgIsUZrMquefq07ZfeXiXeq2fqCsy9itr9Hg5n25vQDqgbKLMAgAAAMXM5POq2Y41arVlkRzhUdLbb8t5021GxwLKFMosAAAAUIzKZx1V901TVPv3b/RZjds0tcGzWnzvvVJ6ltHRgDKFMgsAAAAUk3q/fKYu70xXqLtAU//dSW/feJ9kMhkdCyiTKLMAAADAxcrO1rNvT1fD797RL5fU0ITGPbSvfBWjUwFlGmUWAAAAuAjWr3ZIz7bTfb/8qjdufUhL7nhMbgt3KwZKGmUWAAAAuBAej8KnTVbk2FFSfLwGNn9e311+o9GpgKBhNjoAAAAAEGjM+/5UzEPJso8arvz7k6VvvqHIAqWMlVkAAACgCGyr35S913OS2y3HizOV/2gLhZWLNjoWEHQoswAAAEAhmLKzZO/fW2FLl8h1S4Ic01+Wt3oNo2MBQYsyCwAAAJyHdfsXiu74jMx//K6cHn2U27OvFMJNngAjUWYBAACAs3G7FTE5TRETxsp7aRVlrNogd73bjU4FQJRZAAAA4IzMv+9VdKe2CvniMzlTmit73ET5omOMjgXgvyizAAAAwN/Yli+VvW9PSZJjxsvKf/hRgxMB+DvKLAAAAPBfJkem7H16KOzNN+S6rZ4c02fLW7Wa0bEAnAFlFgAAAJBk/exTRXduK/P+fcrpO1C53XpKVn5cBvwV/zoBAAAQ3FwuRUwYo4jJE+S9/AplrN0kd8JtRqcCcB6UWQAAAAQt8+5diu70jEJ2bJfzsVRlvzBOPnuU0bEAFAJlFgAAAMHH55Nt6RLZ+/eWrFY5Xp6v/GYpRqcCUASUWQAAAAQVU8Zx2Xs9p7A1K1Vwx13Kmj5b3iqXGR0LQBFRZgEAABDwoqLDFWY7+4+2BS6PJCnkk48V1bmdzIcPKXvQcOV17ipZLKUVE0AxoswCAAAg4IXZrEruufqsj68d3ViRI4YqfNpkearXUMaG9+S+uU4pJgRQ3CizAAAAKNMuO/andMcditixQ3ktWyv7+dFSZKTRsQBcJMosAAAAyiSbK1+PfL5MD25fLcVGK3PeYhUkJRsdC0AxocwCAACgbPH59I/fvlDbD+foEke63rvhHt37/msqMEcYnQxAMaLMAgAAoMy4JOOg2n8wR7fu3q49Fa5Q30dG6YfLaureSy6R0rOMjgegGFFmAQAAEPicTj362VI1/3yFPGazXqnfWmvrNJHHwo+7QFnFv24AAAAEtJD335UG9dETv/6qj6+5U3MS2+hYVAWjYwEoYZRZAAAABCTzvj9lH9xftnWrpauv1uCHhunrqrWNjgWglJiNDgAAAAAUicul8GlTVP7OWxX67ibl9B8sffstRRYIMqzMAgAAIGCEbN0ie98esv70o/Ib3a/skWPlvaKqIm02o6MBKGWUWQAAAPg906FDsg8fpLDlS+W5oqoyFy5VQcPGRscCYCDKLAAAAPyX262w+XMUOXqkTPlO5fTordyuPaUIvjMWCHaUWQAAAPgl6/YvZO/TQyHffaOCf/5L2aPHy1PjaqNjAfATlFkAAAD4FdOxo4ocOUzhi16Vp/KlypzzqgqSH5BMJqOjAfAjlFkAAAD4B69XYYsXKHLkUJkcDuV26qrcXn3ls0cZnQyAH6LMAgAAwHDWb76WvW8PhezYroJ6dyh77ER5rr/B6FgA/BhlFgAAAIYxZWYocsxIhc2bI1/5CnJMe0n5zR/jkmIA50WZBQAAQOnz+WRbvlT2YYNkOnpEzqeeUU6/QfLFxBqdDECAoMwCAACgVFl+3Cl7v54K3bpFrrq3KPu15XLfVNvoWAACDGUWAAAApSM7W5FpYxQ+e4Z8UVHKmvCinKmtJLPZ6GQAAhBlFgAAACXL55OWL1f5bs/Jsn+f8lJbKWfQcPkqVDA6GYAARpkFAABAiQn5bKsiRo+QPv1E3lo3yfHyfLlv/YfRsQCUAZRZAAAAXLSo6HCF2U760XLbNmnwYGnTJik+Xu4Xpyrj4VTJyo+fAIoH7yYAAAC4aGE2q5J7rla19D1K3bpE9X77Qo6wKC2v31rrb26s5V0ekdKzjI4JoAyhzAIAAODi/fSTer01QXf/tEW5tnAtvKOF1tZNVl5ouNHJAJRRlFkAAABcMPPvexUxYay0dIlus4Rq+W0PaeUtzZQdHmV0NABlHGUWAAAARWY+sF8Rk8YrbPGCv75ap1s3tc27WZkRsUZHAxAk+FIvAAAAFJopPV2RQwao/D9qK2zRq3KmttKxL/4jTZxIkQVQqliZBQAAwHmZMo4rfMZURcyeKTnzlP/I48rp2VfeqtWMjgYgSFFmAQAAcFam7CyFz56p8BlTZXZkyvngQ8rtPUCeq642OhqAIEeZBQAAwOlycxU+b44ipk6U+dgx5TdKUk7fgfLUrGV0MgCQRJkFAADAyfLzFbboVUVMTpPl0EEV3HOvcvoOlLtugtHJAOAUlFkAAABIbrfCli5RxISxsvz5hwrq3aGs2fPkuv1Oo5MBwBlRZgEAAIKZxyPbqhWKGPeCrLt3yVWnrrImvCjXP/8lmUxGpwOAs6LMAgAABCOfT6FvrVXkuFGy/rhT7htqKXPB6ypo2JgSCyAgUGYBAACCic+n0PffUcTokQr55mu5r7pajtnzlN/0QclsNjodABQaZRYAACBIhGzZrMjRIxSy7XN5rqgqx4szlf/wo5KVHwkBBB7euQAAAMoyn08hWzZLMyYr9r335ImvrKxxk+Rs0VIKDTU6HQBcsEJfS5Kdna0mTZrozz//lCRt3bpVycnJatCggSZNmnRiv507dyolJUUNGzbUwIED5Xa7JUn79+9XamqqGjVqpI4dOyonJ0eS5HA41K5dOzVu3FipqalKT08vzvMDAAAITk6nbK8tUrl77lTsQ//X3r3HR1neeR//ziGZyQlCYEIAQVQqQSsgRFCrICigSDip9YDa+rSKIq2yrdX2WXG7dqs+S5daFbG6uq1t16WuUpCDUhFti1ZAJSpgVVDOSSAcJskkc7iv548JQ8JBETKZzFyf9+uVV5J7knzuK7aX+Tn3zJRL77+v2vsfUM3f31PDt7/DIAsg7R3TMLt27Vpde+21+uyzzyRJDQ0N+slPfqI5c+Zo8eLF+uCDD/T6669Lku666y7NnDlTL7/8sowxmjdvniTppz/9qa677jotXbpUX//61zVnzhxJ0i9/+UuVlZVpyZIluuqqq/Rv//ZvSVgmAACAHVxVVcr99wfUedCZ6nDHNMk4Cs5+VPr8c4Wm3i7l5KT6FAGgVRzTMDtv3jzdd999Ki4uliRVVFTo5JNPVs+ePeX1elVeXq6lS5dq27Ztamho0MCBAyVJkydP1tKlSxWJRLRq1SqNGTOmxXFJWrFihcrLyyVJ48aN0xtvvKFIJNLqCwUAAMhkHTZ/osDdd6jLoDOU9+8PyD3kHGnZMnk//EAFd96usCcr1acIAK3qmB4ze+i9pVVVVQoEAonPi4uLVVlZedjxQCCgyspK7dmzR/n5+fI2PbnAgeOH/iyv16v8/HzV1NSoa9euJ7YyAACATOc4yn71FeXMnaPsv6xQozdbr545UgvOLte2oh7SkjppyQJJ0sJfTEjxyQJA6zquJ4ByHEeuZq8/ZoyRy+U66vED75s79PPm3+P+Ck8L37lz/lc8+5YCgYIT+v4TRZ8+ffva9OnTt7ffau26Ouk3v5Eeflj6xz+kHj2kBx7QtzefpNqcozds/t3Tp5/q/pfJiL2pjfvHNcyWlJS0eKKm6upqFRcXH3Z8165dKi4uVlFRkYLBoGKxmDweT+Lrpfi9urt27VJJSYmi0ajq6upUWFh4zOeye3etHMcczzIUCBSoujp4XN/bGujTp5+avs1rp0+ffnrvPe5tW5Xz9JPyP/uM3Hv3KnL2IIXm/qcayycq0L1ItT/40xd+fzLXfix/jNKnn6n9L5PK80v17+ZE+m636wvvvDyuV8YeMGCANm3apM8//1yxWEwvvfSShg0bph49esjn82nNmjWSpD/96U8aNmyYsrKyVFZWpsWLF0uS5s+fr2HDhkmShg8frvnz50uSFi9erLKyMmVl8ZgOAACAA7zvrFbB1JtUVHaWch57WJELhmvPwle0d+lrapx8lcTfTgAsdFz3zPp8Pj344IP63ve+p8bGRg0fPlyXXnqpJGnWrFn653/+Z9XW1urMM8/UjTfeKEm67777dM899+jxxx9Xt27d9B//8R+SpDvuuEP33HOPLr/8chUUFGjWrFmttDQAAIA0Fo0qe/FC5T4xR1mr/i6noINCN9+m0HdukXNy71SfHQCk3FcaZpcvX574+LzzztOCBQsO+5rS0lI9//zzhx3v0aOHnn322cOOFxYWau7cuV/lNAAAADKWa/8++X/3W+X85xPybNms2Mm9VfuzB9Vw7fUyBR1SfXoA0G4c1z2zAAAAaF3uTRuV89Rc+f/wO7nrahU+7xuqvf9BhcdcJnk8qT49AGh3GGYBAABSxRhlvfk35cx9TNkvL5a8XjVOvEKhqdMU7T8w1WcHAO0awywAAEArKOiQI7/v6H9ahSOxZp+E5XvxeeX8+nFlvb9WTlGR6u/8gRpuullOSbc2OFsASH8MswAAAK3A7/Oq/AteGmfhLybItXu3cn7zn/I//aQ8VZWKnt5XwVkPq+HKq6Xc3DY8WwBIfwyzAAAAyWSMvlb5iXTzzer8u9/J1dCg8IiLFfzV44qMuFhyuVJ9hgCQlhhmAQAAkqBTbY0uWv+6Llm3XL12b5H8fjVcdY1Ct0xTrG9pqk8PANIewywAAEAr8UYjGrJxlS75cLkGffaOPMbR+m599eglt2n6H3+u2gjPSgwArYVhFgAA4EQYI+/ad6U//VG/feo3Kmis1a78zvrfcyZp+Rkjta2ohyRpemGhVB1M8ckCQOZgmAUAADgOrspK+Z//H/n/5/fyblgv+f16p/c5evXMkVrbq78cN/fCAkAyMcwCAAAcq8ZGZb+ygvOcugAAIABJREFURP7nfq/s5X+WKxZTpGyIgrMeVsF3btSs+19P9RkCgDUYZgEAAL6IMfJWvCf/c7+X74U/yr1nj2Il3RS6/Q41XH2dYl87XZJUUFiQ4hMFALswzAIAAByBq7JS/v+dF7+MeP06GZ9PjWPHqeHqKYoMHyF5uIwYAFKJYRYAAOCAxkZlv7JU/v/5vbJfXRa/jHjwOQr++y/VOHGyTMfCVJ8hAKAJwywAALDbMV5GDABoXxhmAQCAlbiMGADSG8MsAACwRzis7JcWxC8j/vMrTZcRlyn4/2bHLyMu7JTqMwQAHCOGWQAAkNliMWW9/ZZ8C16U5v+vOu7erVjXEoWmfT9+GfHpfVN9hgCA48AwCwAAMkZBhxz5fV4pHJaWL5deeEGaP1+qrpZ8PsXGT1DwiqsVGTZC8vJnEACkM3ZxAACQGerq5P/LMr32f3+lIRtXKS9cr/osv1afWqY3h3xLa04ZrHmPXKNIdTDVZwoAaAUMswAAIG259u5R9itL5Vu0UNkrXpVCIQ32F+hvXztPb37tPK3t1V8Rb3aqTxMAkAQMswAAIK24KivlW7pIvkULlPXXN+SKRhXr1l0N192gnCnX6IYFe+S4eSZiAMh0DLMAAKDdc2/+XL7FC+VbtFDet9+SyxhFTzlVoVunq/HyckXPHiy53coJFMh56U+pPl0AQBtgmAUAAO2S5x8fybdogbIXLVRWxXuSpOiZZ6n+rh+r8fLxipX2k1yuFJ8lACBVGGYBAED7YIy8a9+NP/510QJ5P/lYkhQpG6La+36mxrHj5JxyaopPEgDQXjDMAgCA1Gl6DdjsRQvkW/ySPFu3yHg8ipx/oYLfvVXhsePklHRL9VkCANohhlkAANC2wmFl/fV1+RYtlG/JS3Lv2iXj8yk84mLV/egnCo++VKaoc6rPEgDQzjHMAgCApHPt26usN1ZIy19W5wUL5Q7ul5OXr/DoMWq8fLzCI0dJ+fmpPk0AQBphmAUAAK0vEpH3nTXKXvGqslcsl/fdNXI5jtS5sxrLJyh8ebnCF14k+f0pPlEAQLpimAUAACfOGLk3bVT2iuXKfv01Zf31DbmD+2XcbkXPHqz6O3+oyEUjVXjZxardE0r12QIAMgDDLAAAOC6ufXuV9Zc3lL1iubJXLJdn82fxG3r3lq69Rho9Wq6RI5XVqZOymr4nHIml5FwBAJmHYRYAABybo1w67OQXKHLBMNVP+54Krhiv8jkfxF//daWklW+0+BELfzEhFWcOAMhADLMAAODIvuzS4Rl3KTx8pKKDy6Ss+H2vBYECyfVhas8bAGAFhlkAAJBwtEuHY71OVuPEKxS+aKQiFw6TKeyUytMEAIBhFgAAqx3DpcPhi0bKOeXU+KXDAAC0EwyzAADYxBi5N36qr3LpMAAA7RHDLAAAmcwYuT//TFl/f1NZb78l/fV1dd60SRKXDgMA0hvDLAAAmSQSkfeDCmW9/Zay/v6WvG+/JU9VpSTJ6dBRGnGRglOnc+kwACDtMcwCAJDGXMH98q5elbjnNeud1XLV10uK3/MaGXaR6oecq8jQ8xTrW6pA145qqA6m+KwBADhxDLMAAKQR97atBwfXv78lz/oP5XKc+GNev95foSk3KjrkXEWGnCunW/dUny4AAEnDMAsAQHsVi8mzfl18eF3VNLxu2ypJcvLyFR18jhp/cLciQ85VdHCZTH5Bik8YAIC2wzALAEB7UVenrHfXJO559a5eJXdwvyQpVtJNkaHnqf727ys65FxFz/i65D38X+MFHXLk9x39X+/hSCxppw8AQFtimAUAIEVclZXxy4XfbhpeK9bKFYvJuFyKlZ6hxslXKTK06ZLhnr2O6cma/D6vyn/wp6PevvAXE1pzCQAApAzDLAAAbSEalefjf0jzK1Tw59eU9fZb8nwWf4kck5OjyNmDVf/9GfHHu5YNkelYmOITBgCgfWOYBQCgtUUi8ny0Qd731ypr7bvyrn1P3nUfyBUKSZKyuwQUGXKuQjfdrMiQoYqeNUDKzk7xSQMAkF4YZgEAOBHhsLwfrY8PrGvfk/f99+T98AO5GhslSU5+gaJn9Vfoxv+jaP8B6jDqIu3u2JXXdwUA4AQxzAIAcKwaG+Vd/2F8aK1YK2/Fe/Ku/1CucFiS5HToqGj/AQr9n1sUHTBQ0QEDFTvlNMntPvgzAgUSr/MKAMAJY5gFAOBIQqFmg2vTva4b1skVjUqSnMJCRc8aqNAt0xTtP0CR/gPl9D5FBYV5yuXZhAEASDqGWQAA6uvl/fD9+NBasVZZa9+T56P1csXig6fTqZOi/QcqNO37igwYqGj/gXJ6nXzES4V5NmEAANoGwywAwC61tfK+9aay3n8vca+r5x8fyeU4kiSnSxdF+w9U45hLFT0rfqmwc1JPHuMKAEA7wzALAMhM4bA8n34i74Z18ny0Xt4NG+T5aL208VN1MkaS5ASKFRkwUI1jyxUdcHZ8cO3WncEVAIA0wDALAEhvkYg8mzbGB9b16+T9KD60ejZ+mnh8q3G7FTv1NMVKz5D3+ina1+eM+OBa0i3FJw8AAI4XwywAID3EYvJ8tlGeDRvk/Wi9PBuaBtdPPpYrEpEkGZdLzsm9FS3tp8ax5Yr1LVW0bz/F+nxN8vslSYFAgcI8mzAAAGmPYRYA0L44jtyff5a4h9W7Yb08H22Q9+OPEq/dKkmxXicr2rdU4YtHK1raT7HSfor2OV3KzU3hyQMAgLbCMAsASA1j5N66Jf6Y1gP3th4YWuvrE18W63GSYn1LFbpw+MGh9Wt9pfz8I/7Ygg458vPSOAAAZDyGWQBAcoVC8ny2SZ6Nn8qz8VNp6yYVrq2Q56OP5K6rTXxZrGuJYqX9FLrh24r17ado31LF+pbKdOj4lXK8NA4AAHZgmAUAnLiGBnk+/ywxsHo2firPpqb327e1/NriYpnTS9V4zXWKlp4Rf0xraalMYafUnDsAAEhLDLMAgGPT2CjP5s9bDqxNQ6t721a5ml7uRpKcoiLFTjlNkfMvUMOpp8WfSfjU0xQ75VR16dNT+3gCJgAAcIIYZgEAB4XD8mw50sC6Ue6tW+RynMSXOoWFip16miJDz0sMqrFTT1POgK/LXxKQW1LWkRI8ZhUAALQChlkAsE0kIs/GT+KvzXrI0OreukWu2MFh0+nQUbFTT1Wk7BzFrrqmxT2spqjzEX+8P1DAY1YBAEDSMcwCQKYJh+Xevk2eLZvl3rpFni2bm328Rdq+VUXRaOLLnYIO8XtYBw1W7IqrFDvlwGXBfWSKiiSXK4WLAQAAODKGWQBIN6GQPNu2yr35c3m2bokPqc0+du/Y3uLxq8blktOtu5yTeipyzhB5Sqdof9eTEkOr6dKFgRUAAKQdhlkAaGdctUG5t2yRZ+tmuTdvjg+pWzbLs3WzPFu2yF1d1eLrjdcrp3sPxXr2UuTC4fL2OU3e006ReveWTj5ZrpNOkic7Wx7FH8MajsTUuLf+SGkAAIC0wTALAG3JGKmmRt7318UH1i2fN92z2nQZ8NbNcu/Z0/JbfD7Fepwkp2cvNY65TM5JPRXr2UtOz17x9yXdJI8n8fWBA49Z/WC/pPeb3g7iMasAACATMMwCQGsJh+XeuUPuHTvkrtwhz47t8Y93bpd75065d2yXZ+cOKRRS81dUNbm5ivXspdhJPRUdXKbYSb3k9Ip/7vTsJSdQLLndKVsWAABAe8QwCwBfxhi5amqahtEDA2rT247t8jQNr+5duw7/Xp9P6tFD6t5dGnKO1KOHoiXdVNe5pGlg7cWTLAEAABwHhlkAdguF5N65Q56mwbT5PameHdsTQ6srHD7sW50uAcW6dVesRw9FBpXJ6dZNTrfuKig9Td/7w0fanV+koL/gsEF14V0TFK4OttUKAQAAMhLDLIDME43KvXuXXNXVcldXNb1Vy70r/rn21ajT5viz/rr37j3s201urmIl8cE0cs7Q+DMBl5Qo1q27nKbjTnFXKTv7iPmCQIE+W9aY7FUCAABYjWEWQHpoaEgMo+7qKrl37ZIrMai2HFZdNTUtXprmAOPzxR9/2q1Esd6nKnLu+XK6dY8PrgeG1G7dZAo6cNkvAABAO8cwCyA1jJGrNihXVXwwdTcfTHdVx4fT6qqmgbVa7uD+I/4YJ79ApksXOYFixU7to8jQ8+UEAnK6BOQEiuUEimWK4x+b/Pglv4FAgfZzmS8AAEBaY5gF0DpCIblrdsefKKlmt9x7ahIfu/bUyL272bE9NVJ1lbqEQkf8UU6nTolBNHbWADmBgEzT5/EhNZD4WLm5R/wZBR1y5PcdeYsLR2KttmwAAACkBsMsgJaMkerqDg6kTUNo80HVtadG7po9ctU0u+0og6kkOQUdZIqK5BQVyenSRbGvnS5Prx6qzSuMD6bFxfFhtUtATucuR30s6lfh93njr7V6BLzOKgAAQPpjmAUy1YHLePfulWvfPrn3xd+79u2Ve99eqbFO+Vt2NN1bulvumpqmIXX3EZ+5V5KMyyVTWCinU5FMUWc53bsr9vWz5HSKD6qmqHP8ts6dldOzm3zdS6SiIrmzsiRJnmY/KxyJKbS3vg1+EQAAAMhEDLNAexYONxtE9zYNovviA+r+fXI3f99sUHUdGFwd5+g/2+ORr1MnOUWdZToVKXZybzmDBst0KoofK4q/PzCcOp2KZAoLJY/n6D+zmcJAwVHvGZW4dxQAAAAnhmEWSJYDl+vWBuUKBuUK7o+/379frtqgFGtQ7rbKgwNqs/eJAbb+i++5ND6fnI6FMh07ynQsjF/Ce1ofmcJCZQW6yBvoLBUWSp06HfY+nJOnffsb2uiXAQAAALQuhlngUI4jV11t0wDabAitDcq9f//Bz5uOuYJBuRPHDrnti+4ZlZQnyenQUaZjx/hQWlgo59Q+cgoLZTp0jH/eNKjGv6aTTGFh4uvl9x/1ZwcO3DO6/cCRYNPbZkkH7hllmAUAAEB6ahfD7MKFC/X4448rGo3qW9/6lqZMmZLqU0I6cRy56uvkqquLD6F1Bz+O3zNa2+K4mj5WpEEdd9UcHDz3xwdRd+2xvWSLyc2TU1Agc+Atv0P85V8KCuQUFCi3JKCnX9+ikC9X9dk5qs9ueu/LVSgrR0/N+qaqG13HfNkuAAAAgINSPsxWVlZq9uzZeuGFF5Sdna1rrrlGQ4cOVZ8+fVJ9ajhBh700ijFSQ4NUXy/V1yv8/mZ5d+46OITW1h5xIP3Cj+vrvvRS3BY8HqmgQMrLkynoIFdenkx+gZxu3ZsNph2a3g4Oqk5+y89NfoHk/eL/++QGCvTiFzxmVJ06SbzWKQAAAHBcUj7Mrly5Uueee64KCwslSWPGjNHSpUs1ffr0FJ9Z+vui19mUDnmtTWOkxsb4cBgKNb3VS3X1coXqE5+76uOf68DndUe/LauxQZXbdskXCcsXbZQv0ii3TCKZ3fR2NMbvl8nLk8nLj7/PjX/sdAm0PH6UjwtPKtbtc95WKNuvhiy/Qlk5inq8ksslKX6Z7V6GSQAAACAtuYwx5su/LHmeeOIJ1dfXa8aMGZKkP/7xj6qoqND999+fytNKvUhECoXi92SGQgffvujzQ26L1YfkaWw46veZUCh+uW3TPaX6qv9TcLulvDwpNzf+1vzj3FzFcnLlyT/67U5OjtwdO0r5+Ye/5eV96T2fXyYciSk76+iX8MZijjwe9wk16Kdn3+a106dPP3l9m9dOn77t/S/jRMNye49+N86X3Z6u7WT3U37PrOM4cjXdUyZJxpgWn3+Z3btr5TjHN48HAgWqPvSeOWOkSESuxgapMdz0vlGuxka5wo2JY65wo9Rw4Fiz2w891tgQf3mVxqZj4Ua5GuLHsmMRRYN1UmODXA0NcjWE5Ao1xD+PxY580sfAZGfL+HMkv18xf46M35f43PhzZAIdZPw58hcWKOTOksnJlcnJid/zmZsjNf88Jydxu/JyD35tTq6UnZ24l7PVfv+SFJYUDh33zz3hfhuhn7q+zWunT58+ew99+vTba78x49on2ne7XercOf+o35XyYbakpESrV69OfF5dXa3i4uKkd90bP5VG36SiXbsTw2Vi0GwFxu2OD48+n0y2T/L5ZXzZUrYvPlxm+6SOHRXr1EXG75P8OfHLapve67CPj+1z+f3xe0yPgT9QoFouswUAAACQhlI+zJ5//vl65JFHVFNTo5ycHL3yyittcomxycuXysoUCdbLHBg0fX6Z7GzJ5zvsmPH744NodnZ8eMzOlvH5m77Wl3gfH1x9x3SJbCBQoP0MkwAAAADwlaV8mO3atatmzJihG2+8UZFIRFdeeaX69++f9K7p2lV68kkFGSYBAAAAIO2kfJiVpPLycpWXl6f6NAAAAAAAaSJ1T+kFAAAAAMBxYpgFAAAAAKQdhlkAAAAAQNphmAUAAAAApB2GWQAAAABA2mGYBQAAAACkHYZZAAAAAEDaYZgFAAAAAKQdhlkAAAAAQNphmAUAAAAApB2GWQAAAABA2mGYBQAAAACkHYZZAAAAAEDaYZgFAAAAAKQdhlkAAAAAQNphmAUAAAAApB1vqk/gRLndrpR+/4miT5++fW369Onb27d57fTp02fvae3vcxljzHH9ZAAAAAAAUoTLjAEAAAAAaYdhFgAAAACQdhhmAQAAAABph2EWAAAAAJB2GGYBAAAAAGmHYRYAAAAAkHYYZgEAAAAAaYdhFgAAAACQdhhmAQAAAABph2EWAAAAAJB2GGYtVV9fn+pTQArt2bOnzZuRSETV1dXau3dvm7fRfrD32I29B7Zi77Mbe1/yWDPM7tixQ9OmTdPkyZM1Z84cxWKxxG1Tp05Nej8YDGr27Nl6+umnVVlZqWuuuUaDBg3SzTffrMrKyqT3DzVlypQ2a/3yl7+UJO3fv18//OEPNXToUH3jG9/Qfffdp9ra2qT3t23bpn/6p3/S5s2btWPHDt1www06++yzdf3112vz5s1J7w8aNEiLFy9OeudoduzYobvuukszZ87Uli1bVF5errFjx2rUqFHasGFD0vu7d+/WbbfdprPPPlvDhg3TZZddpqFDh2rmzJlW/Mudvacl9h72Hlv2nlAopFmzZumSSy7RWWedpQEDBmjUqFG6//77FQwGk95PtVTufe1t35PY+9j72PuStfe5jDGm1X9qO3TTTTdp3Lhx6tu3rx599FHFYjE9/vjj8nq9mjhxoubPn5/U/rRp03TaaaepsrJSb7/9tm677TaNHz9eixcv1rJlyzR37tyktc866yxFo1FJkjFGLpdLB/6xu1wurV+/PmltSZo0aZJefPFF3XXXXerWrZu++93vynEc/e53v9P69ev12GOPJbV/3XXXacKECZo0aZLuuOMOjRw5UuXl5Vq+fLmeffZZ/fd//3dS+xdffLF69Oih3Nxc/fCHP1SfPn2S2jvUjTfeqNGjR6u+vl7/9V//pX/5l3/R6NGjtWbNGv3iF7/QH/7wh6T2b731Vk2YMEEjRozQSy+9pNraWo0fP15PP/20tm3bptmzZye1/+ijj37h7dOnT09qn72HvYe9x8695/bbb9eZZ56pyZMnKxAISJKqq6s1f/58rVmzRk8++WRS+zbvfanc9yT2PvY+9r423fuMJSZOnJj42HEcM2PGDHPnnXcaY4yZMGFC0vvl5eXGGGPC4bA5//zzj3puyfDhhx+aa6+91rz88suJY22x5gMOrG/cuHGH3TZ27Ng26xtjzBVXXNHitiOdUzL6juOYefPmmYsvvth85zvfMS+88ILZvHmzaWxsTHq/+T/rCy+8sMVt48ePT3r/0MakSZMSH1922WVJ78+ePdsMHDjQPPzww+aRRx457C3Z2HvYe4xh77Fx7/mixuWXX570vs17Xyr3PWPY+9j72PuOJhl7nzWXGXu9Xn388ceS4v9V7KGHHlJNTY1mzpzZ4tKXZPY3btyorKwsPfPMM4nj69atk8vlSmr7jDPO0NNPP62VK1fqxz/+serq6pLebK66ulqLFy9WSUmJ3nvvvcTxiooK+Xy+pPeLi4s1b948SVJZWZlef/11SdJf/vIXFRYWJr0vxf83d9VVV+mVV17RDTfcoHfeeUfTpk3T0KFDk97Oz8/Xc889p6eeekqxWEyvvfaaJGnNmjVt8vvPysrSqlWrJEkrV65UXl6eJOn999+X3+9Pev/OO+/U2LFjlZOTo+nTpx/2lmzsPew9EnuPjXtPUVGRlixZIsdxEseMMVq0aJE6deqU9L7Ne18q9z2JvY+9j72vTfe+Vh+P26nVq1ebESNGmAULFiSO1dXVmdtuu82UlpYmvb9q1SozatQoE41GE8eWLVtmLrzwQrNmzZqk9w/485//bK688kozatSoNmu++OKL5v777zff/OY3zfTp040xxjzzzDPmggsuMKtXr056v6qqynz729825513npk0aZIpLS01ZWVlZty4cWbjxo1J77flf409kq1bt5q77rrL/OAHPzCbN2821157rRk6dKgZPny4Wbt2bdL7a9euNRdddJE599xzzciRI82HH35oNmzYYCZNmtQmfWOMCQaD5sUXX2yT1qHaw94zevRo9p5me883vvGNlO89mzZtSnq/Pe89FRUVSe8fuvd88MEHib2nLfrbt283U6dONYMGDTLDhw83F110kRk8eLCZOnWq2bZtW9L7xti797WXfc8YY1599dV2s/e1h7+72Pvabu8777zzDvu7KxP3PmseM3tAJBJRVlZWi2Pr169Xv3792vxcwuGwvF6v3O62vYO8urpaK1as0FVXXdWm3eZqa2uVm5vbpmvfs2ePtmzZomg0qkAgoJ49e7ZJt6amRkVFRW3SOlapOKf2+HtoS+w90q5du7RixQpdeeWVbdptjr0ntWzce6LRqPbs2SPHcdS5c2d5vd6UnUsqhMNhZWdntziWir0vVfueFN/7XnvtNf7uYu/L+GZzbbX3WTPMBoNBPfLII9qxY4cuueQSTZgwIXHbvffeq/vvvz9j+8FgUL/61a+0c+dO69beXvr8/lPXdxxH8+bN09KlS7Vz50653W4VFxdr2LBhuuGGGw4bMNuqP3z4cF1//fVt1l+yZIkqKyvbtH+0tm2/+1T3+f2npg8ANmrrv/usGWanT5+u008/XX379tWvf/1rnXHGGYlf5oFnfcvUvs1rp0//3nvvleM4mjRpkoqLi2WMUXV1tRYsWKD6+nrNmjWLfga26dNPdf/Lnq134sSJ9DOwTZ++7f22/rvPmmtdtm7dmnia+uHDh2vq1Kl68MEHdc8996gt5vlU9m1eO336q1at0tKlS1scO/nkk1VWVqaxY8fSz9A2ffqp7r/55pt65ZVXdOmllx7x9mT/QWlz3+a106ef6n5b/91nzTArxR8rGggE5Pf79eijj2rKlCmaO3dumz3DXCr7Nq+dvt39vLw8VVRUqH///i2Ov/vuu4ln+KOfeW369FPdf+ihh7Rv3z4NHjw4JY8Tt7lv89rp0091X2rjv/ta/Sml2qlly5aZCy64wCxbtixxrLKy0kyaNMn069cvo/s2r50+/XXr1plx48aZUaNGmeuuu85MmTLFjBo1yowbN85s2LCBfoa26dNPdd+Y+F731FNPtUmLfvtp06dvc7+t/+6z5jGzUvyZ3KLRaIvXuHIcR8uXL9cll1yS0X2b106fviRt375dVVVVchxHJSUl6t69e5t06du9dvr0AcA2bfp3X6uPx2nklltusbZv89rp06fP3kOfPn27+javnT79TO63/YtttSNVVVXW9m1eO3369Nl76NOnb1ff5rXTp5/JfauHWZPiK6xT2bd57fTp02fvoU+fvl19m9dOn34m9616zOyhqqqqVFxcbGXf5rXTp3/gWfbo29WmT5++vX2b106ffib3rblndseOHZo2bZomT56sOXPmKBaLJf6Ynjp1akb3bV47ffrBYFCzZ8/W008/rcrKSl1zzTUaM2aMbr75ZlVWVtLP0DZ9+vTt7du8dvr0betbM8z+5Cc/0cUXX6x//dd/VUVFhW699VZFIhFJapN/sKns27x2+vTvvvtuOY6jDRs26Oqrr9akSZP0t7/9TZdeeqnuu+8++hnapk+fvr19m9dOn751/aQ9tVQ7M3HixMTHjuOYGTNmmDvvvNMYY8yECRMyum/z2unTLy8vN8YYEw6Hzfnnn3/Uc6OfWW369Onb27d57fTp29a35p5Zr9erjz/+WJLkcrn00EMPqaamRjNnzlQsFsvovs1rp0/f6/Vq48aNysrK0jPPPJM4vm7dOrlcLvoZ2qZPn769fZvXTp++df1WH4/bqdWrV5sRI0aYBQsWJI7V1dWZ2267zZSWlmZ03+a106e/atUqM3r0aBONRhPHli1bZi688EKzZs0a+hnapk+fvr19m9dOn75tfeuezTgSiSgrK6vFsfXr16tfv34Z37d57fTpNxcOh+X1euV2p+biFJv7Nq+dPn367D306dNvXd5W/4ntlOM4mjdvnpYuXaqdO3fK7XaruLhYw4cP1/XXX5/RfZvXTp/+gf6SJUtUWVl5WP/QAZt+ZrTp06dvb9/mtdOnb1vfmntm7733XjmOo0mTJqm4uFjGGFVXV2vBggWqr6/XrFmzMrZv89rp06fP3kOfPn27+javnT596/qtfuFyOzVmzJij3nbZZZdldN/mtdOnT5+9hz59+nb1bV47ffq29a15NuO8vDxVVFQcdvzdd99VXl5eRvdtXjt9+vTZe+jTp29X3+a106dvW9+ay4zXr1+vH/3oR2psbFQgEJDL5VJVVZV8Pp9mzZqlvn37Zmzf5rXTp0+fvYc+ffp29W1eO336tvWtGWYP2L59u6qqquQ4jkpKStS9e3dr+javnT59+uw99OnTt6tv89rp07em3+oXLqeRW265xdq+zWunT58+ew99+vTt6tu8dvr0M7lvzWNmj6Sqqsravs3yjGaSAAAFJUlEQVRrp0+fPnsPffr07erbvHb69DO5b/Uwa1J8hXUq+zavnT59+uw99OnTt6tv89rp08/kvnWPmW2uurpagUDAyr7Na6dPnz57D3369O3q27x2+vQzue9Nyk9th0KhkB577DEtXbpUlZWVcrvdKi4u1rBhw3TnnXeqoKAgY/s2r50+ffrsPfTp07erb/Pa6dO3rW/NPbO33367zjzzTE2ePDnxXwaqq6s1f/58rVmzRk8++WTG9m1eO3369Nl76NOnb1ff5rXTp29dP2lPLdXOXHbZZUe97fLLL8/ovs1rp0+fPnsPffr07erbvHb69G3rW/MEUEVFRVqyZIkcx0kcM8Zo0aJF6tSpU0b3bV47ffr02Xvo06dvV9/mtdOnb1vfmsuMd+zYoZ/+9KdatWqVCgoK5HK5FAwGVVZWppkzZyb9hYRT2bd57fTp02fvoU+fvl19m9dOn75tfWuG2QOi0aj27Nkjx3HUuXNneb1t+xxYqezbvHb69Omz99CnT9+uvs1rp0/flr41lxkHg0H9/Oc/14wZM7Ry5Up17do18Uu99957M7pv89rp06fP3kOfPn27+javnT592/rWDLM//vGPlZ+fr3Hjxum3v/1ti1/mBx98kNF9m9dOnz599h769Onb1bd57fTpW9dv9aeUaqcmTJiQ+DgUCpkbb7zRPPDAA4fdlol9m9dOnz599h769Onb1bd57fTp29a35p5ZKf4aR5Lk9/v16KOPauXKlZo7d65cLlfG921eO3369Nl76NOnb1ff5rXTp29Vv9XH43Zq2bJl5oILLjDLli1LHKusrDSTJk0y/fr1y+i+zWunT58+ew99+vTt6tu8dvr0betbM8waY0wwGDR79+5tcSwWi7X4ZWdq3+a106dPn72HPn36dvVtXjt9+jb12/Y5mlPs3Xff1dKlS7Vz50653W4VFxdr+PDhGj16dMb3bV47ffr02Xvo06dvV9/mtdOnb1PfmteZffjhh1VRUaHx48eruLhYxhhVV1frpZdeUp8+fXT33XdnbN/mtdOnT5+9hz59+nb1bV47ffrW9Vv9vt52avTo0SYWix12PBqNmksvvTSj+zavnT59+uw99OnTt6tv89rp07etb82zGft8Pu3cufOw49u3b1d2dnZG921eO3369Nl76NOnb1ff5rXTp29b35rHzN5zzz2aMmWKevfurUAgIJfLpaqqKn322Wd64IEHMrpv89rp06fP3kOfPn27+javnT592/rWPGZWkhYtWqRNmzbJ4/HopJNOUklJiQYMGKAXX3xRV199dUb3bV47ffr02Xvo06dvV9/mtdOnb1PfmsuMZ82apeeff1579uzRs88+K8dxdM455yg7O1vPPfdcRvdtXjt9+vTZe+jTp29X3+a106dvXb/VH4XbTo0bN85EIhFjjDGbNm0yI0aMMIsXLzbGGDNhwoSM7tu8dvr06bP30KdP366+zWunT9+2vjWPmTXGyOVySZJ69+6tJ554QjfddJOKiooSxzO1b/Pa6dOnz95Dnz59u/o2r50+fev6rT4et1OPPPKIufbaa83atWsTx1avXm3OPfdcM2jQoIzu27x2+vTps/fQp0/frr7Na6dP37a+NcOsMcasXLnSfPLJJy2Obd++3fzsZz/L+L7Na6dPnz57D3369O3q27x2+vRt6lv1bMYAAAAAgMxgzbMZAwAAAAAyB8MsAAAAACDtMMwCAAAAANIOwywAAAAAIO0wzAIAAAAA0s7/B0x9gsE8VgBfAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "FUTURE_DAYS = 3\n",
    "\n",
    "# Append N new days to our indices\n",
    "date_format = '%Y-%m-%d'\n",
    "date_range = [datetime.datetime.strptime(date, date_format) for date in country.index]\n",
    "for _ in range(FUTURE_DAYS): date_range.append(date_range[-1] + datetime.timedelta(days=1))\n",
    "date_range = [datetime.datetime.strftime(date, date_format) for date in date_range]\n",
    "\n",
    "# Perform projection with the previously estimated parameters\n",
    "projected = [0] * len(X) + [exponential_function(x, *params) for x in range(len(X), len(X) + FUTURE_DAYS)]\n",
    "projected = pd.Series(projected, index=date_range, name='Projected')\n",
    "df_ = pd.DataFrame({'Confirmed': country['Confirmed'], 'Projected': projected})\n",
    "ax = df_.plot(kind='bar', figsize=(16, 8))\n",
    "estimate = [exponential_function(x, *params) for x in range(len(date_range))]\n",
    "ax.plot(date_range, estimate, color='red', label='Estimate')\n",
    "ax.legend();"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}